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ORCIDStephen Mussmann
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- affiliation: University of Washington Computer, WA, USA
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2020 – today
- 2024
[c16]Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon S. Du, Kevin G. Jamieson, Jordan T. Ash, Robert D. Nowak:
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. ACL (Findings) 2024: 6549-6560- [i17]Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey A. Bilmes, Simon S. Du, Kevin G. Jamieson, Jordan T. Ash, Robert D. Nowak:
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. CoRR abs/2401.06692 (2024) - [i16]Ruhui Jin, Qin Li, Stephen O. Mussmann, Stephen J. Wright:
Continuous nonlinear adaptive experimental design with gradient flow. CoRR abs/2411.14332 (2024) - 2023
- [j1]Maureen Daum, Enhao Zhang, Dong He, Stephen Mussmann, Brandon Haynes, Ranjay Krishna, Magdalena Balazinska:
VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building. Proc. VLDB Endow. 16(13): 4188-4201 (2023) - [c15]Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah M. Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander J. Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt:
DataComp: In search of the next generation of multimodal datasets. NeurIPS 2023 - [i15]Maureen Daum, Enhao Zhang, Dong He, Stephen Mussmann, Brandon Haynes, Ranjay Krishna, Magdalena Balazinska:
VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building. CoRR abs/2303.04068 (2023) - [i14]Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah M. Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt:
DataComp: In search of the next generation of multimodal datasets. CoRR abs/2304.14108 (2023) - [i13]Jifan Zhang, Yifang Chen, Gregory Canal, Stephen Mussmann, Yinglun Zhu, Simon Shaolei Du, Kevin G. Jamieson, Robert D. Nowak:
LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning. CoRR abs/2306.09910 (2023) - 2022
- [c14]Stephen O. Mussmann, Sanjoy Dasgupta:
Constants Matter: The Performance Gains of Active Learning. ICML 2022: 16123-16173 - [i12]Stephen Mussmann, Julia Reisler, Daniel Tsai, Ehsan Mousavi, Shayne O'Brien, Moisés Goldszmidt:
Active Learning with Expected Error Reduction. CoRR abs/2211.09283 (2022) - 2021
- [b1]Stephen Mussmann:
Understanding and analyzing the effectiveness of uncertainty sampling. Stanford University, USA, 2021 - [c13]Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré:
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation. AISTATS 2021: 3286-3294 - [i11]Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré:
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation. CoRR abs/2103.02761 (2021) - 2020
- [c12]Stephen Mussmann, Robin Jia
, Percy Liang:
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks. EMNLP (Findings) 2020: 3400-3413 - [c11]Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia:
Selection via Proxy: Efficient Data Selection for Deep Learning. ICLR 2020 - [c10]Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang:
Concept Bottleneck Models. ICML 2020: 5338-5348 - [c9]Ray Li, Percy Liang, Stephen Mussmann:
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree. SODA 2020: 102-121 - [i10]Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang:
Concept Bottleneck Models. CoRR abs/2007.04612 (2020) - [i9]Stephen Mussmann, Robin Jia, Percy Liang:
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks. CoRR abs/2010.05103 (2020)
2010 – 2019
- 2019
- [i8]Ray Li, Percy Liang, Stephen Mussmann:
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree. CoRR abs/1906.11385 (2019) - [i7]Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia:
Selection Via Proxy: Efficient Data Selection For Deep Learning. CoRR abs/1906.11829 (2019) - 2018
- [c8]Arun Tejasvi Chaganty, Stephen Mussmann, Percy Liang:
The price of debiasing automatic metrics in natural language evalaution. ACL (1) 2018: 643-653 - [c7]Stephen Mussmann, Percy Liang:
Generalized Binary Search For Split-Neighborly Problems. AISTATS 2018: 1561-1569 - [c6]Stephen Mussmann, Percy Liang:
On the Relationship between Data Efficiency and Error for Uncertainty Sampling. ICML 2018: 3671-3679 - [c5]Stephen Mussmann, Percy Liang:
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. NeurIPS 2018: 6955-6964 - [i6]Stephen Mussmann, Percy Liang:
Generalized Binary Search For Split-Neighborly Problems. CoRR abs/1802.09751 (2018) - [i5]Stephen Mussmann, Percy Liang:
On the Relationship between Data Efficiency and Error for Uncertainty Sampling. CoRR abs/1806.06123 (2018) - [i4]Arun Tejasvi Chaganty, Stephen Mussmann, Percy Liang:
The price of debiasing automatic metrics in natural language evaluation. CoRR abs/1807.02202 (2018) - [i3]Stephen Mussmann, Percy Liang:
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. CoRR abs/1812.01815 (2018) - 2017
- [c4]Stephen Mussmann, Daniel Levy, Stefano Ermon:
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. UAI 2017 - [i2]Stephen Mussmann, Daniel Levy, Stefano Ermon:
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. CoRR abs/1707.03372 (2017) - 2016
- [c3]Stephen Mussmann, Stefano Ermon:
Learning and Inference via Maximum Inner Product Search. ICML 2016: 2587-2596 - 2015
- [c2]Stephen Mussmann, John Moore, Joseph John Pfeiffer III, Jennifer Neville:
Incorporating Assortativity and Degree Dependence into Scalable Network Models. AAAI 2015: 238-246 - [i1]Mahdi M. Kalayeh, Stephen Mussmann, Alla Petrakova, Niels da Vitoria Lobo, Mubarak Shah:
Understanding Trajectory Behavior: A Motion Pattern Approach. CoRR abs/1501.00614 (2015) - 2014
- [c1]Stephen Mussmann, John Moore, Joseph J. Pfeiffer III, Jennifer Neville:
Assortativity in Chung Lu Random Graph Models. SNAKDD 2014: 3:1-3:8
Coauthor Index
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